{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the data set\n",
    "dataset=pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.iloc[:,:-1].values\n",
    "y=dataset.iloc[:,1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.1],\n",
       "       [ 1.3],\n",
       "       [ 1.5],\n",
       "       [ 2. ],\n",
       "       [ 2.2],\n",
       "       [ 2.9],\n",
       "       [ 3. ],\n",
       "       [ 3.2],\n",
       "       [ 3.2],\n",
       "       [ 3.7],\n",
       "       [ 3.9],\n",
       "       [ 4. ],\n",
       "       [ 4. ],\n",
       "       [ 4.1],\n",
       "       [ 4.5],\n",
       "       [ 4.9],\n",
       "       [ 5.1],\n",
       "       [ 5.3],\n",
       "       [ 5.9],\n",
       "       [ 6. ],\n",
       "       [ 6.8],\n",
       "       [ 7.1],\n",
       "       [ 7.9],\n",
       "       [ 8.2],\n",
       "       [ 8.7],\n",
       "       [ 9. ],\n",
       "       [ 9.5],\n",
       "       [ 9.6],\n",
       "       [10.3],\n",
       "       [10.5]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
       "       122391., 121872.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=1/3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
       "        76349.68719258, 100649.1375447 ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualsing the training set results\n",
    "plt.scatter(X_train,y_train,color='red')\n",
    "plt.plot(X_train,regressor.predict(X_train),)\n",
    "plt.title('Salary vs Experience(Train set)')\n",
    "plt.xlabel('Experience in years')\n",
    "plt.ylabel('Salary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualising the test set results\n",
    "plt.scatter(X_test,y_test,color='red')\n",
    "plt.plot(X_train,regressor.predict(X_train),color='blue')\n",
    "plt.title('Salary vs experience(Test set)')\n",
    "plt.xlabel('Experience in years')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
